Transillumination Imaging Research Articles

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.

Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.

Transformer Connections: Improving Segmentation in Blurred Near‐Infrared Blood Vessel Image in Different Depth

AbstractHigh‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Atlas of Dental Near-Infrared Transillumination Images.

Technological improvements have introduced significant innovations in dentistry and broadened the array of tools and techniques in dental care. One technological development that has been widely researched over the past 20 years is the use of Near-Infrared Transillumination (NIRT) imaging for the diagnosis of dental caries. This paper aims to introduce a comprehensive collection of NIRT images, intended as a reference tool for routine dental examinations, dental research, pedagogical activities, and forensic odontology. The collection presents pairwise clinical and NIRT images categorized as follows: (a) healthy teeth, (b) carious teeth, (c) restored teeth, (d) enamel defects, and (e) diverse findings. This atlas could be a valuable tool for the dental community as it is designed as an identification guide of NIRT illustrated dental features.

Reconstructing 3D De-Blurred Structures from Limited Angles of View through Turbid Media Using Deep Learning

Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18% compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82%. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues.

Three-dimensional transillumination imaging for understanding unroofed coronary sinus defect.

Three-dimensional transillumination imaging for understanding unroofed coronary sinus defect.

Near-Infrared Transillumination for Macroscopic Functional Imaging of Animal Bodies.

The classical transillumination technique has been revitalized through recent advancements in optical technology, enhancing its applicability in the realm of biomedical research. With a new perspective on near-axis scattered light, we have harnessed near-infrared (NIR) light to visualize intricate internal light-absorbing structures within animal bodies. By leveraging the principle of differentiation, we have extended the applicability of the Beer-Lambert law even in cases of scattering-dominant media, such as animal body tissues. This approach facilitates the visualization of dynamic physiological changes occurring within animal bodies, thereby enabling noninvasive, real-time imaging of macroscopic functionality in vivo. An important challenge inherent to transillumination imaging lies in the image blur caused by pronounced light scattering within body tissues. By extracting near-axis scattered components from the predominant diffusely scattered light, we have achieved cross-sectional imaging of animal bodies. Furthermore, we have introduced software-based techniques encompassing deconvolution using the point spread function and the application of deep learning principles to counteract the scattering effect. Finally, transillumination imaging has been elevated from two-dimensional to three-dimensional imaging. The effectiveness and applicability of these proposed techniques have been validated through comprehensive simulations and experiments involving human and animal subjects. As demonstrated through these studies, transillumination imaging coupled with emerging technologies offers a promising avenue for future biomedical applications.

Improvement of the Performance of Scattering Suppression and Absorbing Structure Depth Estimation on Transillumination Image by Deep Learning

The development of optical sensors, especially with regard to the improved resolution of cameras, has made optical techniques more applicable in medicine and live animal research. Research efforts focus on image signal acquisition, scattering de-blur for acquired images, and the development of image reconstruction algorithms. Rapidly evolving artificial intelligence has enabled the development of techniques for de-blurring and estimating the depth of light-absorbing structures in biological tissues. Although the feasibility of applying deep learning to overcome these problems has been demonstrated in previous studies, limitations still exist in terms of de-blurring capabilities on complex structures and the heterogeneity of turbid medium, as well as the limit of accurate estimation of the depth of absorptive structures in biological tissues (shallower than 15.0 mm). These problems are related to the absorption structure’s complexity, the biological tissue’s heterogeneity, the training data, and the neural network model itself. This study thoroughly explores how to generate training and testing datasets on different deep learning models to find the model with the best performance. The results of the de-blurred image show that the Attention Res-UNet model has the best de-blurring ability, with a correlation of more than 89% between the de-blurred image and the original structure image. This result comes from adding the Attention gate and the Residual block to the common U-net model structure. The results of the depth estimation show that the DenseNet169 model shows the ability to estimate depth with high accuracy beyond the limit of 20.0 mm. The results of this study once again confirm the feasibility of applying deep learning in transmission image processing to reconstruct clear images and obtain information on the absorbing structure inside biological tissue. This allows the development of subsequent transillumination imaging studies in biological tissues with greater heterogeneity and structural complexity.

Diagnostic Performance of Multispectral SWIR Transillumination and Reflectance Imaging for Caries Detection.

The aim of this clinical study was to compare the diagnostic performance of dual short wavelength infrared (SWIR) occlusal transillumination and reflectance multispectral imaging with conventional visual assessment and radiography for caries detection on premolars scheduled for extraction for orthodontics reasons. Polarized light microscopy (PLM) and micro-computed tomography (microCT) performed after tooth extraction were used as gold standards. The custom-fabricated imaging probe was 3D-printed and the imaging system employed a SWIR camera and fiber-optic light sources emitting light at 1300 nm for occlusal transillumination and 1600 nm for reflectance measurements. Teeth (n = 135) on 40 test subjects were imaged in vivo using the SWIR imaging prototype in the study and teeth were extracted after imaging. Our study demonstrates for the first time that near-simultaneous real-time transillumination and reflectance video can be successfully acquired for caries detection. Both SWIR imaging modalities had markedly higher sensitivity for lesions on proximal and occlusal surfaces compared to conventional methods (visual and radiographic). Reflectance imaging at 1600 nm had higher sensitivity and specificity than transillumination at 1300 nm. The combined SWIR methods yielded higher specificity but the combined sensitivity was lower than for each individual method.

Recent innovations and technological advancements in Dentistry and Medicine

The newer innovations play an important role in the earlier diagnosis of dental caries based on near-infrared transillumination imaging such as Diagnocam and Soprocare intraoral digital cameras, which aid in the diagnosis of dental caries and also differentiate old and newer dental plaques, respectively. Nanoparticles used as carriers for effective and targeted drug therapies in the treatment of rheumatoid arthritis of the temporomandibular joint (TMJ) and candidiasis among chronic denture wearers provided better-increased bioavailability than oral formulations. Artificial intelligence (AI) uses machines to simulate intelligent human behavior. Augmented reality has been presented as a cognitive extension of AI in health care, emphasizing the auxiliary and supplemental roles of dental and medical practitioners. The article enlightens readers with newer diagnostic methods in dentistry and medicine and innovations such as Surface Topography-Assisted Robotic Superstructures (STARS), Prostate-Specific Membrane Antigen Ligand Positron Emission Tomography/Computed Tomography (PSMA-PETCT), toothbrushes with augmented reality, new hydrogel-based electrodes for Brain-Computer Interface (BCI) applications, and robotics in dentistry.

Modeling light propagation in a reconstructed breast model using the Monte Carlo method

Breast cancer is one of the most common types of cancer that causes the highest death among women. One of the main aspects of effectively monitoring the effectiveness of breast cancer treatment is the early detection of abnormalities in breast tissue. It is one of the most critical factors that may determine how well breast cancer therapy is carried out. On the other hand, the techniques used in breast diagnostics today have several limitations, including the hazards connected with radiation exposure, the high price, and the likelihood of creating false-positive results. Therefore, optical techniques are considered feasible strategies for the early diagnosis of breast abnormalities. The optical-based technique has been studied to develop self-diagnostic aids and high-tech devices such as optical tomography. The recent development of therapeutic applications using low-power light also creates a new direction in treating benign tumors through photobiological effects. Therefore, much more research must be conducted to understand how light interacts with breast tissue for developing the diagnosis device and treatment device. In this study, light interaction with tissue was investigated by simulating light propagation at wavelengths of 650 nm, 800 nm, and 950 nm in a breast model that was generated using an MRI data collection. Specifically, the tissue's absorption of these three wavelengths was evaluated to understand better how light interacts with breast tissue. The results of the simulation show that normal breast models and sick breast models have significantly different total quantities of energy absorbed, and these results indicate this discrepancy. The distribution map acquired correlates with the image obtained using optical transillumination imaging. The simulation results can steer further research toward developing a novel breast diagnostics methodology based on optical approaches.

Reconstructing a Deblurred 3D Structure in a Turbid Medium from a Single Blurred 2D Image-For Near-Infrared Transillumination Imaging of a Human Body.

To provide another modality for three-dimensional (3D) medical imaging, new techniques were developed to reconstruct a 3D structure in a turbid medium from a single blurred 2D image obtained using near-infrared transillumination imaging. One technique uses 1D information of a curvilinear absorber, or the intensity profile across the absorber image. Profiles in different conditions are calculated by convolution with the depth-dependent point spread function (PSF) of the transillumination image. In databanks, profiles are stored as lookup tables to connect the contrast and spread of the profile to the absorber depth. One-to-one correspondence from the contrast and spread to the absorber depth and thickness were newly found. Another technique uses 2D information of the transillumination image of a volumetric absorber. A blurred 2D image is deconvolved with the depth-dependent PSF, thereby producing many images with points of focus on different parts. The depth of the image part can be estimated by searching the deconvolved images for the image part in the best focus. To suppress difficulties of high-spatial-frequency noise, we applied a noise-robust focus stacking method. Experimentation verified the feasibility of the proposed techniques, and suggested their applicability to curvilinear and volumetric absorbers such as blood vessel networks and cancerous lesions in tissues.

Towards Trustworthy AI in Dentistry

Medical and dental artificial intelligence (AI) require the trust of both users and recipients of the AI to enhance implementation, acceptability, reach, and maintenance. Standardization is one strategy to generate such trust, with quality standards pushing for improvements in AI and reliable quality in a number of attributes. In the present brief review, we summarize ongoing activities from research and standardization that contribute to the trustworthiness of medical and, specifically, dental AI and discuss the role of standardization and some of its key elements. Furthermore, we discuss how explainable AI methods can support the development of trustworthy AI models in dentistry. In particular, we demonstrate the practical benefits of using explainable AI on the use case of caries prediction on near-infrared light transillumination images.

Advances in Procedural Echocardiographic Imaging in Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.

Transcatheter edge-to-edge repair (TEER) therapy is recommended by the American College of Cardiology/American Heart Association (ACC/AHA) guidelines for selected patients with symptomatic severe or moderate-severe mitral regurgitation (MR). Echocardiography, in particular transesophageal echocardiography (TEE), plays a critical role in procedural planning and guidance for TEER. Recent innovations and advances in TEE techniques including three-dimensional (3D) imaging, unlimited x-plane imaging, live 3D multiplanar reconstruction, as well as transillumination imaging with color Doppler and transparency rendering have further enhanced procedural imaging for TEER, especially for complex diseases including commissural defects, clefts, and multi-segment pathologies. This review discusses the technology of these advanced procedural imaging techniques and provides a “step-by-step” guide on how to apply them during the TEER procedure with a focus on their added values in treatment of complex valve lesions.

Evaluating interproximal and occlusal lesion severity with a dual SWIR transillumination/reflectance probe.

We have developed a clinical probe capable of acquiring simultaneous, multispectral short wavelength infrared (SWIR) reflectance and occlusal transillumination images of lesions on tooth proximal and occlusal surfaces to reduce the potential of false positives and enhance diagnosis. The dual probe was 3D printed and the imaging system uses an InGaAs camera and broadband light sources at 1310 nm for occlusal transillumination and 1600 nm for cross-polarization reflectance measurements. In this study a mathematical model to estimate the penetration depth of "hidden" occlusal lesions from the SWIR images was developed. We compared the model's estimated lesion depth on 18 extracted teeth with lesions against microCT measurements. Although the model estimated depth deviates from that measured in microCT at higher depths, there is a good linear correlation (R2 = 0.93) between the estimated depth from SWIR images and the measured depth using microCT. SWIR occlusal transillumination images at 1300 nm also provide information about interproximal lesion penetration depth which can be directly viewed from the occlusal surface. SWIR occlusal transillumination and reflectance depth measurements on 49 natural interproximal lesions were compared with microCT measurements. There was significant correlation between the depths measured with SWIR occlusal transillumination (R2 = 0.81) and reflectance (R2 = 0.19) compared with the depths measured with microCT.

Measurement of the Depth of Lesions on Proximal Surfaces with SWIR Multispectral Transillumination and Reflectance Imaging.

The aim of this study was to compare the diagnostic performance of dual short-wavelength infrared (SWIR) transillumination and reflectance multispectral imaging devices for imaging interproximal lesions with radiography using extracted teeth that had been imaged with micro-computed tomography (microCT). Thirty-six extracted teeth with 67 lesions on the proximal surfaces were imaged using a newly fabricated SWIR multispectral proximal transillumination and reflectance imaging device along with an existing SWIR multispectral occlusal transillumination and reflectance device. The ability of SWIR imaging and radiography to detect lesions and accurately assess lesion dimensions were compared using microCT as a standard. Occlusal and proximal transillumination and occlusal reflectance performed best for imaging interproximal lesions while proximal reflectance was not useful for imaging interproximal lesions from tooth buccal and lingual surfaces. There was high correlation of the lesion dimensions measured in occlusal and proximal transillumination images compared to microCT and moderate correlation in occlusal reflectance images. The correlation between the lesion depth measured in radiographs and the lesion depth measured with microCT was not significant. This study demonstrates that SWIR occlusal and proximal transillumination and SWIR occlusal reflectance images are useful for imaging interproximal lesions and they provide more accurate measurements of lesion severity.

Microscopic Imaging Methods for Organ-on-a-Chip Platforms.

Microscopic imaging is essential and the most popular method for in situ monitoring and evaluating the outcome of various organ-on-a-chip (OOC) platforms, including the number and morphology of mammalian cells, gene expression, protein secretions, etc. This review presents an overview of how various imaging methods can be used to image organ-on-a-chip platforms, including transillumination imaging (including brightfield, phase-contrast, and holographic optofluidic imaging), fluorescence imaging (including confocal fluorescence and light-sheet fluorescence imaging), and smartphone-based imaging (including microscope attachment-based, quantitative phase, and lens-free imaging). While various microscopic imaging methods have been demonstrated for conventional microfluidic devices, a relatively small number of microscopic imaging methods have been demonstrated for OOC platforms. Some methods have rarely been used to image OOCs. Specific requirements for imaging OOCs will be discussed in comparison to the conventional microfluidic devices and future directions will be introduced in this review.

The applications of real-time imaging with transillumination, ultrasound, and Doppler for thread lifting

Thread lifting has gained popularity in recent years because of its minimally invasive properties. Regardless of the technique used, the key is threading in the optimal anatomical plane and suspending the correct target tissue. Failure to meet these objectives may result in chronic pain; contour irregularity; thread migration or exposure; and nerve, vessel, and gland injuries. The knowledge of facial anatomy alone is insufficient to corroborate the anatomical variations of a patient. Ultrasound-guided thread lifting can be performed because the trocar presents a hyperechogenic signal with bayonet and reverberation artifacts. Ultrasound is an effective tool because it can provide real-time images of the layers of the facial anatomy, fat pads, muscles, fascia, ligaments, superficial muscular aponeurotic system, arteries, and parotid duct. Transillumination, however, can be conducted to verify the presence of superficial vessels and prevent venipuncture and injury to homonymous arteries. The combination of transillumination and ultrasound provides three-dimensional information. In this study, to evaluate facial anatomy and guide threading, reconfirm the position of the thread, and prevent malpractices, practical strategies such as transillumination, ultrasound, and Doppler imaging are recommended for improving patient safety during, before, and after the procedure.

Assuring Quality of Scaffolds in Musculoskeletal Tissue Engineering

Abstract In order to achieve the high quality required in medical products, reliable characterization methods and quality management systems are necessary. In the field of musculoskeletal Tissue Engineering (mTE), electrospinning is utilized to manufacture fibre scaffolds as implant material. Depending on the application, in this case the regeneration of tendon-bone junctions, properties like the degree of fibre orientation, homogeneity of fibre throughout the scaffold and reaction to external mechanical load are of particular importance. Currently, destructive methods, like scanning electron microscopy (SEM), are widely used to determine these properties. In addition to the destruction of the samples, these methods often only allow the investigation of very small sections. In this study, we present two new methods for the fast, non-destructive and contactless characterization of electrospun fibre scaffolds for mTE. These methods are based on Transillumination Imaging (TI) and Mueller Matrix Polarimetry (MMP), utilizing low-power laser sources or LED light sources, respectively, to determine the relative homogeneity (TI) and the degree of fibre orientation (MMP) in electrospun fibre scaffolds.

Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging

Vein recognition technology identifies human vein characteristics under near-infrared light and compares it with stored vein information for personal identification. Although this has high anti-counterfeiting performance, it is possible to fabricate artificial hands that simulate vein characteristics to deceive the identity authentication system. In view of this potential deficiency, we introduced heart rate information to vein authentication, a means of living body detection, which can further improve the anti-counterfeiting effect of vein authentication. A hand vein transillumination imaging experiment was designed to prove its effectiveness. In the proposed method, a near-infrared light source is used to transilluminate the hand, and the transillumination images are collected by a common camera. Then, the region of interest is selected for gray-scale image processing, the feature value of each frame is extracted by superimposing and averaging the images, and then the one-dimensional pulse wave is drawn. Furthermore, the baseline drift phenomenon is filtered by morphological methods, and the maximum percentage frequency is determined by Fast Fourier Transform, that is, the pulse wave frequency. The heart rate value is then calculated, and finally, the stability of the heart rate detection result is evaluated. The experiment shows that the method produces accurate and stable results, demonstrating that it can provide living information (heart rate value) for vein authentication, which has great application prospects and development opportunities in security systems.

Feasibility study for realization of optical transillumination imaging of arteriovenous fistula

AbstractDaily management of arteriovenous fistula for dialysis patients is crucially important because it sometimes leads to stenosis and obstruction. Numerous shortcomings are associated with management techniques used today. Aside from being invasive, they also require the use of inspection, auscultation, and palpation. Moreover, they rely on the extensive experience of technicians. Actually, definite diagnosis can be made using angiography, but angiography entails difficulties associated with radiation and bulky equipment. To overcome these difficulties posed by current management techniques, we proposed an optical imaging technique. Unfortunately, the related apparatus was too large and heavy for bedside use. This paper therefore explains the possibility of clinical application of our novel technique using compact and light apparatus. Contrast of blood vessel transillumination images is intensified with two wavelengths used for measurements. Results indicate improved accuracy of inner diameter measurements of blood vessels. This study confirmed the possibility of applying this technique in medical settings to achieve continuous management of AV fistula.